Overview

Dataset statistics

Number of variables17
Number of observations3955
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory364.2 KiB
Average record size in memory94.3 B

Variable types

Categorical5
Numeric9
Boolean3

Alerts

rental_year has constant value "2021" Constant
rental_date has a high cardinality: 212 distinct values High cardinality
rental_month is highly correlated with tempHigh correlation
temp is highly correlated with rental_monthHigh correlation
rental_month is highly correlated with tempHigh correlation
temp is highly correlated with rental_monthHigh correlation
rental_hour is highly correlated with rental_year and 1 other fieldsHigh correlation
rental_day is highly correlated with rental_year and 1 other fieldsHigh correlation
rental_month is highly correlated with rental_year and 1 other fieldsHigh correlation
rental_year is highly correlated with rental_hour and 3 other fieldsHigh correlation
dayofweek_n is highly correlated with rainHigh correlation
rain is highly correlated with rental_hour and 4 other fieldsHigh correlation
working_day is highly correlated with dayofweek and 2 other fieldsHigh correlation
dayofweek is highly correlated with working_day and 1 other fieldsHigh correlation
peak is highly correlated with working_day and 1 other fieldsHigh correlation
rental_year is highly correlated with working_day and 5 other fieldsHigh correlation
holiday is highly correlated with rental_yearHigh correlation
season is highly correlated with rental_yearHigh correlation
timesofday is highly correlated with rental_yearHigh correlation
rental_hour is highly correlated with peak and 3 other fieldsHigh correlation
rental_month is highly correlated with season and 1 other fieldsHigh correlation
dayofweek_n is highly correlated with dayofweek and 1 other fieldsHigh correlation
dayofweek is highly correlated with dayofweek_n and 1 other fieldsHigh correlation
working_day is highly correlated with dayofweek_n and 2 other fieldsHigh correlation
season is highly correlated with rental_month and 1 other fieldsHigh correlation
peak is highly correlated with rental_hour and 1 other fieldsHigh correlation
timesofday is highly correlated with rental_hourHigh correlation
temp is highly correlated with rental_month and 1 other fieldsHigh correlation
rhum is highly correlated with rental_hourHigh correlation
count is highly correlated with rental_hourHigh correlation
rental_date is uniformly distributed Uniform
rental_hour has 131 (3.3%) zeros Zeros
dayofweek_n has 562 (14.2%) zeros Zeros
rain has 3582 (90.6%) zeros Zeros

Reproduction

Analysis started2022-04-09 10:05:39.893642
Analysis finished2022-04-09 10:06:09.261058
Duration29.37 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

rental_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct212
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
2021-07-24
 
24
2021-06-27
 
23
2021-06-20
 
23
2021-08-01
 
23
2021-08-24
 
22
Other values (207)
3840 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-02-01
2nd row2021-02-01
3rd row2021-02-01
4th row2021-02-01
5th row2021-02-01

Common Values

ValueCountFrequency (%)
2021-07-2424
 
0.6%
2021-06-2723
 
0.6%
2021-06-2023
 
0.6%
2021-08-0123
 
0.6%
2021-08-2422
 
0.6%
2021-08-1222
 
0.6%
2021-06-0522
 
0.6%
2021-06-0622
 
0.6%
2021-06-1122
 
0.6%
2021-08-2922
 
0.6%
Other values (202)3730
94.3%

Length

2022-04-09T11:06:09.526197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-07-2424
 
0.6%
2021-06-2023
 
0.6%
2021-08-0123
 
0.6%
2021-06-2723
 
0.6%
2021-06-1122
 
0.6%
2021-06-1922
 
0.6%
2021-08-2922
 
0.6%
2021-06-1222
 
0.6%
2021-06-0622
 
0.6%
2021-06-0522
 
0.6%
Other values (202)3730
94.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rental_hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.19342604
Minimum0
Maximum23
Zeros131
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:09.723627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.156018827
Coefficient of variation (CV)0.4665974408
Kurtosis-0.7010877986
Mean13.19342604
Median Absolute Deviation (MAD)5
Skewness-0.3353604129
Sum52180
Variance37.8965678
MonotonicityNot monotonic
2022-04-09T11:06:09.911302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17212
 
5.4%
13212
 
5.4%
18211
 
5.3%
19210
 
5.3%
12210
 
5.3%
16209
 
5.3%
14208
 
5.3%
10208
 
5.3%
11207
 
5.2%
15207
 
5.2%
Other values (14)1861
47.1%
ValueCountFrequency (%)
0131
3.3%
188
2.2%
281
 
2.0%
348
 
1.2%
438
 
1.0%
539
 
1.0%
6124
3.1%
7170
4.3%
8206
5.2%
9206
5.2%
ValueCountFrequency (%)
23153
3.9%
22183
4.6%
21192
4.9%
20202
5.1%
19210
5.3%
18211
5.3%
17212
5.4%
16209
5.3%
15207
5.2%
14208
5.3%

rental_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.71908976
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:10.116877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.719058433
Coefficient of variation (CV)0.5546796008
Kurtosis-1.192594954
Mean15.71908976
Median Absolute Deviation (MAD)8
Skewness-0.01522254709
Sum62169
Variance76.02197995
MonotonicityNot monotonic
2022-04-09T11:06:10.315987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24142
 
3.6%
27140
 
3.5%
25139
 
3.5%
20138
 
3.5%
19136
 
3.4%
18136
 
3.4%
5134
 
3.4%
26133
 
3.4%
13133
 
3.4%
7133
 
3.4%
Other values (21)2591
65.5%
ValueCountFrequency (%)
1126
3.2%
2129
3.3%
3130
3.3%
4128
3.2%
5134
3.4%
6128
3.2%
7133
3.4%
8132
3.3%
9128
3.2%
10122
3.1%
ValueCountFrequency (%)
3160
1.5%
30108
2.7%
29116
2.9%
28123
3.1%
27140
3.5%
26133
3.4%
25139
3.5%
24142
3.6%
23129
3.3%
22131
3.3%

rental_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.142604298
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:10.563027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median5
Q37
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.98410293
Coefficient of variation (CV)0.3858167602
Kurtosis-1.234041566
Mean5.142604298
Median Absolute Deviation (MAD)2
Skewness-0.08256495849
Sum20339
Variance3.936664435
MonotonicityIncreasing
2022-04-09T11:06:10.715774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8618
15.6%
7600
15.2%
6599
15.1%
5566
14.3%
3555
14.0%
4536
13.6%
2481
12.2%
ValueCountFrequency (%)
2481
12.2%
3555
14.0%
4536
13.6%
5566
14.3%
6599
15.1%
7600
15.2%
8618
15.6%
ValueCountFrequency (%)
8618
15.6%
7600
15.2%
6599
15.1%
5566
14.3%
4536
13.6%
3555
14.0%
2481
12.2%

rental_year
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
2021
3955 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
20213955
100.0%

Length

2022-04-09T11:06:10.894555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T11:06:11.020783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
20213955
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

holiday
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
3785 
True
 
170
ValueCountFrequency (%)
False3785
95.7%
True170
 
4.3%
2022-04-09T11:06:11.084180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

dayofweek_n
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.015423515
Minimum0
Maximum6
Zeros562
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:11.184369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.003667551
Coefficient of variation (CV)0.6644730138
Kurtosis-1.25948173
Mean3.015423515
Median Absolute Deviation (MAD)2
Skewness-0.01552412483
Sum11926
Variance4.014683653
MonotonicityNot monotonic
2022-04-09T11:06:11.482949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5582
14.7%
4572
14.5%
1568
14.4%
6566
14.3%
0562
14.2%
3554
14.0%
2551
13.9%
ValueCountFrequency (%)
0562
14.2%
1568
14.4%
2551
13.9%
3554
14.0%
4572
14.5%
5582
14.7%
6566
14.3%
ValueCountFrequency (%)
6566
14.3%
5582
14.7%
4572
14.5%
3554
14.0%
2551
13.9%
1568
14.4%
0562
14.2%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
Saturday
582 
Friday
572 
Tuesday
568 
Sunday
566 
Monday
562 
Other values (2)
1105 

Length

Max length9
Median length7
Mean length7.136030341
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonday
2nd rowMonday
3rd rowMonday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Saturday582
14.7%
Friday572
14.5%
Tuesday568
14.4%
Sunday566
14.3%
Monday562
14.2%
Thursday554
14.0%
Wednesday551
13.9%

Length

2022-04-09T11:06:11.726000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T11:06:11.914716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
saturday582
14.7%
friday572
14.5%
tuesday568
14.4%
sunday566
14.3%
monday562
14.2%
thursday554
14.0%
wednesday551
13.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
True
2675 
False
1280 
ValueCountFrequency (%)
True2675
67.6%
False1280
32.4%
2022-04-09T11:06:12.058383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

season
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Spring
1704 
Summer
1414 
Winter
837 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring1704
43.1%
Summer1414
35.8%
Winter837
21.2%

Length

2022-04-09T11:06:12.155460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T11:06:12.273190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
spring1704
43.1%
summer1414
35.8%
winter837
21.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
2587 
True
1368 
ValueCountFrequency (%)
False2587
65.4%
True1368
34.6%
2022-04-09T11:06:12.352024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

timesofday
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Afternoon
1258 
Evening
998 
Morning
997 
Night
702 

Length

Max length9
Median length7
Mean length7.281163085
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowMorning
3rd rowMorning
4th rowMorning
5th rowMorning

Common Values

ValueCountFrequency (%)
Afternoon1258
31.8%
Evening998
25.2%
Morning997
25.2%
Night702
17.7%

Length

2022-04-09T11:06:12.461413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T11:06:12.599807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
afternoon1258
31.8%
evening998
25.2%
morning997
25.2%
night702
17.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rain
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05638432364
Minimum0
Maximum10.3
Zeros3582
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:12.942384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3
Maximum10.3
Range10.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3284465882
Coefficient of variation (CV)5.825140163
Kurtosis303.7377589
Mean0.05638432364
Median Absolute Deviation (MAD)0
Skewness13.76925744
Sum223
Variance0.1078771613
MonotonicityNot monotonic
2022-04-09T11:06:13.148799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
03582
90.6%
0.1121
 
3.1%
0.252
 
1.3%
0.332
 
0.8%
0.429
 
0.7%
0.622
 
0.6%
0.521
 
0.5%
0.914
 
0.4%
0.711
 
0.3%
1.111
 
0.3%
Other values (22)60
 
1.5%
ValueCountFrequency (%)
03582
90.6%
0.1121
 
3.1%
0.252
 
1.3%
0.332
 
0.8%
0.429
 
0.7%
0.521
 
0.5%
0.622
 
0.6%
0.711
 
0.3%
0.88
 
0.2%
0.914
 
0.4%
ValueCountFrequency (%)
10.31
 
< 0.1%
5.51
 
< 0.1%
5.11
 
< 0.1%
4.51
 
< 0.1%
3.61
 
< 0.1%
3.51
 
< 0.1%
3.31
 
< 0.1%
2.84
0.1%
2.72
0.1%
2.62
0.1%

temp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct277
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.53605563
Minimum-4
Maximum26.3
Zeros3
Zeros (%)0.1%
Negative35
Negative (%)0.9%
Memory size31.0 KiB
2022-04-09T11:06:13.368532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile2.7
Q17.9
median11.6
Q315.3
95-th percentile19.5
Maximum26.3
Range30.3
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation5.190960843
Coefficient of variation (CV)0.4499770989
Kurtosis-0.329447302
Mean11.53605563
Median Absolute Deviation (MAD)3.7
Skewness-0.05600268498
Sum45625.1
Variance26.94607447
MonotonicityNot monotonic
2022-04-09T11:06:13.593126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.146
 
1.2%
8.942
 
1.1%
13.638
 
1.0%
10.637
 
0.9%
14.937
 
0.9%
7.636
 
0.9%
13.236
 
0.9%
14.335
 
0.9%
10.735
 
0.9%
16.335
 
0.9%
Other values (267)3578
90.5%
ValueCountFrequency (%)
-41
 
< 0.1%
-3.41
 
< 0.1%
-3.31
 
< 0.1%
-2.92
0.1%
-2.51
 
< 0.1%
-2.11
 
< 0.1%
-21
 
< 0.1%
-1.94
0.1%
-1.72
0.1%
-1.61
 
< 0.1%
ValueCountFrequency (%)
26.33
0.1%
26.21
 
< 0.1%
25.91
 
< 0.1%
25.72
0.1%
25.61
 
< 0.1%
25.43
0.1%
25.32
0.1%
25.21
 
< 0.1%
25.12
0.1%
251
 
< 0.1%

rhum
Real number (ℝ≥0)

HIGH CORRELATION

Distinct69
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.08141593
Minimum24
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:13.823344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile54
Q168
median78
Q388
95-th percentile96
Maximum100
Range76
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.25417624
Coefficient of variation (CV)0.1719503472
Kurtosis-0.479698594
Mean77.08141593
Median Absolute Deviation (MAD)10
Skewness-0.4019158076
Sum304857
Variance175.6731877
MonotonicityNot monotonic
2022-04-09T11:06:14.101396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79120
 
3.0%
88117
 
3.0%
86115
 
2.9%
87115
 
2.9%
93111
 
2.8%
71109
 
2.8%
74106
 
2.7%
73106
 
2.7%
82104
 
2.6%
84102
 
2.6%
Other values (59)2850
72.1%
ValueCountFrequency (%)
241
 
< 0.1%
311
 
< 0.1%
321
 
< 0.1%
331
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
381
 
< 0.1%
392
0.1%
404
0.1%
413
0.1%
ValueCountFrequency (%)
10038
 
1.0%
9928
 
0.7%
9840
 
1.0%
9768
1.7%
9671
1.8%
9589
2.3%
9492
2.3%
93111
2.8%
9297
2.5%
9181
2.0%

wdsp
Real number (ℝ≥0)

Distinct26
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.68369153
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:14.375702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q311
95-th percentile17
Maximum26
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.303581518
Coefficient of variation (CV)0.4955935507
Kurtosis0.3861786968
Mean8.68369153
Median Absolute Deviation (MAD)3
Skewness0.7864481832
Sum34344
Variance18.52081389
MonotonicityNot monotonic
2022-04-09T11:06:14.630717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7436
11.0%
6403
10.2%
8397
10.0%
5325
 
8.2%
9315
 
8.0%
4291
 
7.4%
10285
 
7.2%
11241
 
6.1%
3224
 
5.7%
12177
 
4.5%
Other values (16)861
21.8%
ValueCountFrequency (%)
120
 
0.5%
2105
 
2.7%
3224
5.7%
4291
7.4%
5325
8.2%
6403
10.2%
7436
11.0%
8397
10.0%
9315
8.0%
10285
7.2%
ValueCountFrequency (%)
261
 
< 0.1%
254
 
0.1%
242
 
0.1%
234
 
0.1%
2215
 
0.4%
2124
 
0.6%
2029
0.7%
1935
0.9%
1849
1.2%
1771
1.8%

count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.360556258
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2022-04-09T11:06:14.860105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile13
Maximum26
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.854650765
Coefficient of variation (CV)0.7190766367
Kurtosis1.000780293
Mean5.360556258
Median Absolute Deviation (MAD)3
Skewness1.031947252
Sum21201
Variance14.85833252
MonotonicityNot monotonic
2022-04-09T11:06:15.091412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1649
16.4%
2489
12.4%
3429
10.8%
4385
9.7%
5355
9.0%
6328
8.3%
7304
7.7%
8243
 
6.1%
9190
 
4.8%
10155
 
3.9%
Other values (14)428
10.8%
ValueCountFrequency (%)
1649
16.4%
2489
12.4%
3429
10.8%
4385
9.7%
5355
9.0%
6328
8.3%
7304
7.7%
8243
 
6.1%
9190
 
4.8%
10155
 
3.9%
ValueCountFrequency (%)
261
 
< 0.1%
242
 
0.1%
231
 
< 0.1%
211
 
< 0.1%
206
 
0.2%
1910
 
0.3%
188
 
0.2%
1717
0.4%
1619
0.5%
1532
0.8%

Interactions

2022-04-09T11:06:06.734164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:52.997299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:54.485371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:56.318741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:58.026221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:59.770384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:01.891645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:03.392174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:05.054854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:06.889674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:53.186873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:54.660655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:56.474658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:58.184984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:00.170481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:02.064967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:03.546675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:05.251035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:07.046856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:53.354188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:54.890731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:56.637758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:58.340192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:00.484482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:02.229940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:03.727818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:05.501083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:07.209999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:53.518407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:55.107375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:56.792547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:58.504001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:00.668607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:02.416949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:03.899453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:05.769671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:07.367516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:53.678559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:55.265014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:56.953034image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:58.660338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:00.864163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:02.586620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:04.059751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:05.933566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:07.515975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:53.830384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:55.644838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:57.112154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:58.804457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:01.121746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:02.749799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:04.211954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:06.088557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:07.682957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:53.999361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:55.829508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:57.286690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:59.095134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:01.349321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:02.900340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:04.581436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:06.243737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:07.861844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:54.158416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:56.005253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:57.624488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:59.389338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:01.539157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:03.071919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:04.745752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:06.404479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:08.033679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:54.304636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:56.156361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:57.850271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:05:59.563257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:01.709043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:03.239035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:04.896027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-09T11:06:06.577550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-09T11:06:15.313251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-09T11:06:15.627336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-09T11:06:15.898184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-09T11:06:16.186892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-09T11:06:16.479358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-09T11:06:08.541225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-09T11:06:09.056458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

rental_daterental_hourrental_dayrental_monthrental_yearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayraintemprhumwdspcount
02021-02-016122021False0MondayTrueWinterTrueNight0.03.498.031
12021-02-018122021False0MondayTrueWinterTrueMorning0.03.593.042
22021-02-019122021False0MondayTrueWinterTrueMorning0.02.693.024
32021-02-0110122021False0MondayTrueWinterTrueMorning0.04.197.043
42021-02-0111122021False0MondayTrueWinterFalseMorning0.05.286.0612
52021-02-0112122021False0MondayTrueWinterFalseAfternoon0.05.289.079
62021-02-0113122021False0MondayTrueWinterFalseAfternoon0.06.085.079
72021-02-0114122021False0MondayTrueWinterFalseAfternoon0.05.988.0912
82021-02-0115122021False0MondayTrueWinterTrueAfternoon0.05.788.0106
92021-02-0116122021False0MondayTrueWinterTrueAfternoon0.05.389.0105

Last rows

rental_daterental_hourrental_dayrental_monthrental_yearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayraintemprhumwdspcount
39452021-08-31113182021False1TuesdayTrueSummerFalseMorning0.014.572.0135
39462021-08-31123182021False1TuesdayTrueSummerFalseAfternoon0.013.674.0102
39472021-08-31133182021False1TuesdayTrueSummerFalseAfternoon0.013.870.0101
39482021-08-31153182021False1TuesdayTrueSummerTrueAfternoon0.014.273.0114
39492021-08-31163182021False1TuesdayTrueSummerTrueAfternoon0.013.674.0124
39502021-08-31173182021False1TuesdayTrueSummerTrueAfternoon0.013.773.0116
39512021-08-31183182021False1TuesdayTrueSummerTrueEvening0.013.472.0106
39522021-08-31193182021False1TuesdayTrueSummerTrueEvening0.013.169.0103
39532021-08-31203182021False1TuesdayTrueSummerFalseEvening0.013.078.072
39542021-08-31233182021False1TuesdayTrueSummerFalseNight0.013.587.072